{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpath = './data/'\n",
    "data = pd.read_csv(dpath+\"day.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       0\n",
       "dteday        0\n",
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "casual        0\n",
       "registered    0\n",
       "cnt           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0c0a9f22d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.cnt.values, bins=30,kde=True)\n",
    "plt.xlabel('Median value of number of people',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q/hR4nms8gw5tPVSjx4+SiydtqLBYRyAPiApIEHKKKaWzKcVCkCqmq6cL629fDwBW6hotEVwzGyVqcrdGcK9FloVArOmgBUFoP6IGMGldOxUL8wsAFnP2NESrx5qUzGxifmY+F0FggKiABCG3RNXlB+n4eZ6rnV4SnV/x7CK6z+qO5tMfQ9/ftaQL86+Ex6c2E2mcJWQGIAg5xRioxNCmbggLbHLdQZPo/GZfmg1N59wKbA3Nbm3hdkcEgCDklKDKWG5n7hUCQ7uG9Dl7FdRFjen+GyQOQRO1o16YXbBar50M10GIABCEnFKTY1+D3x4wsHEAg9fr7YZdPV1t1+mVV5Zb5jpquqbthggAQcgxAxsHAn3W/Xr/dTevw4a7NtT58Pec02P06y+eXayfOTSiIaH4VCuur75RrdXEYfIUByACQBByjOvaaULXQQ5sHMCaPWtq1EeVUxW88vwr6Orpqlm3uLSId/3Zu7Dhzg011bwGrx/UVtQyCZHyyjJ2LuxsaMRe6i1h8EOD1VG5q6oa3TGK/rX92nZsuHMDNty1wap4fKm3FGulsiwhXkCCkGOCXDuDRrK67RZmF1DqLaFnWQ+mH52u6WiHdg3VpUbou6qvLggNqI8j8LajtLwUOZsnACdv0KPTNa6g049OY3zfOFZvXl2XV8hfPGZ6ahrFpUXMvlR/reZfmcf06XQqsLUaEQCCkGOCXDv9I1lv1LDJpbJyuoLL33M5xm4Zq+lodemfgyqHxZnQrnKqsig0fO2efXkWk4cnjXl7vG3cvWo3pl+qv14zL84AyFclMBcRAIKQY4w1BFaW6zp/mwjf0vISxm6pj86fn5nHwd84aNWx+7Oaju4YxdSDU5g8PNnQ6D8M2xxGNuu5M568CACxAQhCjtG5gupUP0GqIu92Qcy+NOsIG9a7mbq4wsa77tgtYy2LL7At3GK7Xh4qgbnIDEAQ2pCwgi3e38J04KEFXgjV7UY2jVi3cfbl2ZoyjWv2rMHAxgErYRMXXT1d6F/b76h3fOevq0Ewvm88tG15qATmIgJAENoMm4It3t/G942HJoAz4c17H5QpNIzKqUrVTpDkCHp+bh7fue07mJ9x0ju412rqwamazl5nMNYZhf2zp6jZVrOGZAMVhDbDVLCl1FvCmefO6FM5GwqYmPYFLGYOBYAjNxyJRT/fSFGZVmDKGupeJ61NhIDB6wex7uZ1AKJnW00SyQYqCDnFNIKunKoYR+eNJIBzO/97r7s3sPN3/fBtgqump6YzEURlvE5KMJmK3k8enqx+jZptNYuIABCENqMRHTQVCMOF4bokcKZ9uV5CR244Epwfh4Dtz2x3RsUWyoRyXzn10XEQbiSyjcCMq3JamogAEIQ2IyjJmwmeZ613TpiXUJjap7S8hN2rdmO4MGyVxmHmxRmtZ1AzuFG6pd5SXaRyoVjQRi+b4HnG7lW7jcLMKzCNwrONjMRiBBaENsPvR1/uK2PmxRlrHf3sy7M4sPmAcV+2hsxCsYBXpl+pHtfGOFw5VcHBLQfRs6ynGmDVDH7bhs4oC9Sf3+iOUb0dgsz2Cb8BeGjXUGBUs5esGovFCCwIOcBkkAxyafQbLHWdlNH4S0DP2fF04jaUekuYeWGm6s2ja38UTEZe48h/pb7TtunY0zAWixFYEDqImtTPnqRlQWmLvQZLXXDWwS0Hcfl7LteqdgpdhcDOf8NdG2JLmUxdhDV71qDnnMUCMVRYzEPUiEpJd72MNgwCtp3Y1nBnnWVjsaiABCEnmHLvjGwaMXZursHS1ElNHp5EsVSs6+wX5oILp7htCXIztYXnuW4E7WYNbSY/j/96mdpq0ukHxWN495tlY7HMAAQhRxzaegjDhWEMk/P31eu/Guid4xpxTZ309NR0ZDWPN+VzIwZrP2GVyOIaTdumzXCxHdln2VgsAkAQcsKhrYecRG2eDj+s8555YSZwhB7WSRWKhbrva/asqX4Pq0oWRnFp0cq43Oxo2tXlz748W1V5heX+tx3ZRxUsSSICQBBywtG9R+1XJqBnWU+NUdWP20mZiriUeku4+o6ra/ToV99xdV2HObBxANtObLMqwFIoFpzjWdoxXJoZTdfYP+ConNxzD1Ir2Y7sTfaZLHgBiQ1AENqEMI+TKDl6Ln3bpXhk9BHj797kbYATDewNCHNH+kE5//0MbBzA1INTOLr3KHieQV2EVW9dhdPHT4e6R973gfuMwqrZ0XSQKifo3KK4gUa5TkkiAkAQ2gAbg6Mpv42OR75u7vxd3P02Eyvgbb/fpZTnGSf/4aTVaNjkrm5yz4xCo0ZanUBbvXl1Jjt6EyIABKENMI1SR64dqda+7T6rW1vSUEuInKicqmBi/0SNELDp2HSzlKkHpzB265j2mDYj7dEdo9p0FKYEd1GDroxFc0LUShP7JzC+b7wqdHmeMb5vHH1X9RkrrWUpCAwQG4AgtAVBo1G3oIp1529JVM8aXSzBvdfdW2eY9hM20o4yQjfFMwTFCjRqpLXxAmqkPUkiAkAQ2oBGjJzllWXrLJ06ph+djtRRmQrJhxF2bjbG1on9E9i9ajdGrh2JHHTVqJHWRjBlOQgMEBWQILQF1WpcETK3TE9NY93N69B3VV9VBRG1oMu9193r6O5PV0LVF426YoaNtMOMrTb1jG30+VHVMjaqoywHgQEyAxCwOHrSpQsWssHAxoFInT/gBHm52247sQ07F3ZWI2htWZhdcAy3Sn0xcu0Iblpxk/YZadQVMyydQ9AIfWL/BA5sPpBKGUcb1VGWg8AAyxkAEZ0H4AsAfhrOY/gBAD8E8CUAqwCcAPAeZn6WiAjAHgBrAbwM4P3M/G21n80APqF2+4fMvC+2MxEawjacXUif8kr9iNPEzAszNYZcwDxqjYKb0ROofUZ0I/VCsQAiCow3sHnmdCN099kNm9HEGXTlNeiWlpdq1Gt+11kgmqtoGtjOAPYA+Ctm/tcAVgN4GMDHAYwycz+AUfUdANYA6Fd/WwDcAgBEtBzATgA/A+BNAHYS0fkxnYfQIFnXUQqLDO0aqou8DWJ+Zr7uPsaRmgHQPyO6kfrVd1yN9bevrwZzmWoGNPLM2RaXj8s102/QrZyq1Bje5ypzddtkOQgMsJgBENG5AH4OwPsBgJlnAMwQ0XoAb1Wr7QPwDQAfA7AewF+w47j7j0R0HhFdqNZ9gJlPq/0+AOAdAL4Y3+kIUTHqKB+ddnLENOH3nVXXt3bFvX5RbAHufXSvv9+n31UTVU5XUFpen3I5cN+Wemz/6H24MKxtf1S9uO36OtfMRggTOCaX1qwGgQF2KqDXAngawB1EtBrAUQA3AHg1Mz8BAMz8BBG9Sq1/EYDHPNufVMtMy2sgoi1wZg7o6+uLdDJCdIwqAU9hjKhqIVErtQY3mCqqLcB//YM6JL+KI0gg+PXYtvfd+MwxaoRVGLbqLJtYA/956AYvNgInK8ZdW2zmk90ArgBwCzO/EcBLWFT36NDN8Thgee0C5r3MPMjMgxdccIFF8zqXOIy3WpWApjBGlCm6qJXix+1cbat++bG9/l6D8fZntmP97eu1uYB0emzTfT9yw5GaZUFqqCh+8lHUWbYdc5DfvjtbCiIrxl1bbATASQAnmfkh9f0rcATCj5VqB+r/U571L/FsfzGAxwOWCw0QV4BJlMIYti9R1l3f2hFbfXcQjVz/gY0D2P7M9sUCLwF6bNP+3ahi7z6Dkrzpgql0A50omUZtO+ZmBi9ZMu7aEqoCYuYniegxInodM/8QwBCAH6i/zQA+o/7fpza5H8BvEtHdcAy+00pF9DUAf+Qx/L4dwI3xnk7n0GgCKx3NFsbQrdfM9kI9YZ13qbeEucpcoJDQqWx0tgCdzcZvO3A7RFsPI/9z6T5zwzSsXd893zC1kk3hmSgdc6ODl7CcRFm1idm6FPwWgP1E9D0AbwDwR3A6/l8kokkAv6i+A8BhAD8CcBzA/wSwFQCU8fcPAHxL/X3KNQgL0WnlKLvZ/OVZzn/ergQJT9czyJvL3q9w9V9/nUeL19/fP5u0mXEG3V/XGO3fpylK2T1f2xF50HMfxesmyG/f9Bt1EfrX9mN0x6hWHZvldBBWAoCZv6v08v+Gma9m5meZ+RQzDzFzv/p/Wq3LzPxhZv4JZh5g5jHPfm5n5svU3x2tOqlOoJUBJs26rmXd9a0dMem7i2cXQURV24Cby37w+kFj4JQpZYIXfydr0xEPbBww1g4A6ju+0R2jenUjLQoT24GO8X1YWY703AUNXkz3gOcZY7eM1XTwI9eO4NPnfBqHth7SBqplxSYmqSDalFYHmDTrupZl17d2xJSSeXTHaJ3qY/blWRy75xh6lvXULLdJmeDF28nadsRr9qwJPIZXTWkctfPi+dqqE+N6H2xSXx/YfMAqncbMizNOIjwD049OY7gwnKpKSARAmxJHjvYgsqqz7GR0QnXk2hHtulWVDhZH3t2l7kiGZG8na9sR1zyXAXWGA/fpMeraduxxvg+mwYv7TkTJpRSKO2PYNIKpB6ew7uZ18e3bAhEAbUwco2xdRw8gc378IpDqqerQLfqj2ZdnI3X+/k62kepXJsOsa3C22WeUjr2Vs86os6fIMDB261gsAWtREAHQwZg8LHQjxUY9jFrZTqCzA8uMOvQm0Xm0NDLCHto1pFWX+HMUmfbpF/ob7tzQ0g4+6NzicMMNheu9pVoNmUqtZYHBwUEeGzPr0ITmCHKd00LAzoWdLWuP6SW8acVN2gAoU0WoTsGUUsFE8ewiwAjuyGK8xxP7J4wqqrB7pxtxF5cWW+JMoDtWoVjAknOXVN1im02gZ01M15+IjjLzYNh6kg66g4nqMtpKP36Tq9yhrYeM0a+dElhmCoSKej8WZhewevPqwMCpOO9xkJdL2L1LMprcVMjG6xbbaFEdAFaBatV1E46VERVQDrHVl5eWl8ypBXy65bj9+P1tnHlxRvvCH9171LiPTggsC1J/6XToQczPzGPy8CS2ndhmHGHHeY+DOvmwe5dkNLnVPt1kNr53orvUHZieo9RbwsyLM1btSCNWRmYAGaXRPD+Hth7CyKaR0KCTif0TOPPsGfOOfKqF7lJ8YwXdaN/0EgV5XHRCYFlQfh033sKUYlmH29klEath7OQp/N4lWUjFep+Mmuu1evPq0E1mXpixyt/UdVZXKrEyMgPIII0aPSf2T2Ds1voC3H4DbrWQRoTqUKYiII0QxaBGXfoShqXeUkcYgMPy67jXoG4mYPAO8nZ2Nl4zzXhfaWcoBAxePxi6D1uvozi8w2xnUl67xaGth7TvWg0E69Ta82cWazeIF1CH02ienyCvkLBC1TbYegL5X8r+tf2YPDxZ/W5tUCM1A/B3ZuR0gFFSB7crNvl1dN40/Wv7Mb5vvCkVT7PeV8345ttsG5d3mK5Ggj8Ntr8GcVjnXygWsDC7YN2GZtrfDOIFlEGM3h0hHgJBXiHUReAFbt6jIaQNVv7ShtFpqbeEnmU9i0Y31myj0cPmOc1EkCdN0L1wawe46gdducIwjEkBM+J91cr2Bc0swrznTLNWW+Jov60XkMwAMkij2TSDOnf3gWzWnS2sDVazC827UVxarHZQ2heMff8VacYnJMHAxoGajtyLey90My7/6F9XrtBEdX8hkbzG7RIK1muloThIPRb4DlGw3cqGJL3bxAicQRrNphlXvVcAWrc3mzZEeXhLvSWtAbJVpQGzjsnwv2bPGuPzoDOoj9061rALZc3+DOgGAWlkvEzSUOxyaOuh4BViUKgk6d0mAiCDNOqhoS3u0ggaFU2pt2TVhigP75nn6r2QJvZPgArRnK5tKjVlnaAONOh50M64QuxAQR5mYTM40yAgjSpwaaQdD3JLjoOkXUFFBZRRouQ10RX2cOk6qwvzZ+w8EapoOpCeZT1W7Ynim+5VSx3cchBTD05hfN945Cm0P7VAO2LqQEeuHcHojlEM7RrS6oWjqPTKfeVQw2mg776h6MnE/onI6qI4aHVCRB1Bz2apNyCuxoJG7DTNIgKgzfG/0N4HMM7wddsX2X14bVPmurhBX43oT+dn5tveDhB0fU3eIYHJ4AyBfGEeZkEZOnUCyH3+TLRanZF02vEwA29xabHhnEG2g6w4ERVudhVoAAAgAElEQVRQm2Jb2CMuwl5kr1phdMcortxyZWR7RDPGs3a3A4RdX3c2MEyLapuggiqmgjBhhtOoapUglVEeq8BdueVK42+V05XIgXle0niGZQbQRtR4Z1imAY5CqbeEM8+dqeuIu3q66soJuu2ojog87Zl+dBrj+8axevNqHLvnmPW0uBn3uXZPCxFFdebOCIzrMrDu5nU1qkFXFx/mYRZVrRKlFGMeUnqvu3kdvnfn97TpHcp9i9XHGkkdncYzLAKgTaiLPIy583cNxrrOuuecxampX+VU7bA17pmThyex/Zntwb7siuLSIi5+y8V45OuPRD63PIw0bQqpeHHr/+oEZnmlWde/evPq0ACxKGqVIJVRK4K2ssA7b31nYJSyTojOvBicEiKtZ1hUQG2ATeRhs/Sv7TenHTi9+OBGiSJ2S96N7hgNrBULOB3aI6OWnT+h6ilEXYTVm1e3XSfip8aQ31tCV09X6DZu/V8vYbr+ycOTseYAslUZpeEl1CpsvPQGNg5g24lt2LmwE9tObNO68rqu1mnWzJYZQBtw5IYjLe38AeDYPcesAtAi6ynddLpxDjUYcCPYeZ4xvm888UpKcaIz5BeKBVCBAvM1uV45OrXKyCb9jGt6ajq05GEUFY2tyijJ7J5JENX4nIbHkg0iADLOxP6JplzLbKmcqmDuTH20qH8013AqiWhpUSLhd5dM+6WKiikffam3hLnKnHbG5d4XU0cUNZq8GRWNTWdoak8eYjhcwgRo0h5LNogKKOMkOUWefam2o/EHf03sn7DObZ4GSUSftoIg1VtV1QBUvUtsVAZxePPEqaIZ2jWEQrG+u3FjONKg0ZTrpn2ZAvniPE7cyAwgo4TlY0mCuTNz1U7GKv1tBshqbqCg0WHQaL3RUWNc3jxxqWhMOY3SiuGI2ygdVLfBO4vzHsfdLk2VkAiADGKVUTMBZl+axaGthyK5cmaBrOmVwzqbsNz3jbpPxuLNE6NroteZwEvU+xWHO2mjKddNBNVt8BMmGJIUAqICyiCN5utvBWO3jmWn87eMr/F3Wq2agtvu98gNRwLVK0FeJUklWUsir04cydviuh5xz3iiCsrKqUomvKJkBpBBMjWCbaXKxzKYrervbrGuv9Nqlf+57X6DjPje+2warcc9UjWRhJeKaabTv7bfSQFucdy4rkcjRvKga2M6t7CawX6SfvdFAGQMNxtmsznF08QtPlNaXsLcmbk643IVy1O0vRa6ZGWt6kBt9xs0orMZNSbpPtlqLxWbymVhAjqu62FbchKwE/YmAQrURwUHCYako4FFAGSIaq3eNu78dRW6bCKBdVinhgiYSbSqA7Xdb9BxZl7UZzH1jjaNgwFGpJKYjerN407f4Bcyu1ftjiSg47JVRJnx2Ar7IAFqKxiSjgYWAZAh4tL9hwUQ+XEzPTbteaQidEc2LfrkAyGurAGdt7Ug9OQg8o/MWmXcDNtvYLI2ReVUpZoG262ZXFxarJkxBV0DW3VWo2qwJNI3RBXQUUbuYdjOeJodREQRDJINtIOJPColoNBdewu7eroidf7+HCZNVRVjZ1TrGufuve5e3PeB+4wCpbi0iMHrzWVLg7IqmlJL+A1prTJuBu3XpqqWt71jt45VjZomdZnpWtgYDhv18U8ifUNUw3CjxZKSbKMt/nQRabguiwDIEJEfKLdIuncRc2DenUKxYCzFCMTrgbQwu4D5GX0xGvfYfVf1Gbc35brZcNeGwOP6jaut6DAiV+kKwkJe8wIbvaDCBg7GEazK1WTyYErC/tCIgE6640yj8lhSiAooIzQSZUsFwsJsbY4F97uuMIVNxaFEAs8I1eIiu1ftNq4WmOsmwKZABcIwDVdtCGGJ6BrFNLVvhYHWHRw0os4KTN/BZtVOErEBWc2R46Ud2tgoIgAyQKOBXyZVT+V0BRvu3NDQA9tMTn5bbJPLmXLdhPl8u+13//urpLU64KbhfEkmCE0ZDm1qDeiMmnHq24PIYo4cP+3QxkYQAZABQlUGUYu/MBpOjNbqzt82uZzfmGzrWmlDXH70usI45ZWOa2NsaTMIuPRtl9bUfO4udaNyutJwxk5bjymT2+bojlGMbBrJ1Ui4GZrxlEq7SI4IgAwQqjJooCNpZKQbWGMWjkpm+WXLnbz9EXDjAmwDaIDF2Y3uPOJQsTS7D1NhHLca2qVvuzTydXKhAjm2HBVH4d1P5VTFsYPcuSFyOmJ3/d2rdlurdrzbtXNRl1Z1tM1ckyxcT2sjMBF1EdF3iOir6vulRPQQEU0S0ZeIqEctX6K+H1e/r/Ls40a1/IdE9Etxn0y7YqNTbUSP7ffYCEtdYKw7QMCGuzZg24lteN/fvA+DHxqMVPeUF9hosPMbU3X79Z9HHDroZvcRNGubfXkWT373SauiLjqYGRvu3ODkitF4BTXridOoUbNdi7q0Mp1GM9ckC9czihfQDQAe9nz/LIDPM3M/gGcBfFAt/yCAZ5n5MgCfV+uBiF4P4BoAlwN4B4CbiaixNyRnmFLlxoE70gt7CQLrDnDtiGTdzevwyblPYifvxE7eWU1XbCKss/V6dZjsGt4Re1OuqkCNTr1RwmYQlVMVowdUGOW+cqhasJkZTKOeUe1a1KWVHW3QNQkbcGXhelr1OkR0MYB1AL6gvhOAtwH4ilplH4Cr1ef16jvU70Nq/fUA7mbmV5j5EQDHAbwpjpNodwY2DqB7SbA2rnK60rA3yzAN48DmA4EvQWDKgpAOPqhDjmo0tPG5runAEBwvoMUj0BpNFNfsDKJQLGjdOrt6ujC0ayi0E0jDB71V/vCtppUdbdC5j1w7EjjryML1tB127gawHYt1nXoBPMfMbgmpkwAuUp8vAvAYAKjfp9X61eWabaoQ0RYiGiOisaeffjrCqbQvNi6g5b4y1uxZY14hpA80GXfdlyDMGycIf4fsbUt3KZqZSTcbKhQLdW2odmC8E+/e9+5IQsBt56GthzCyKfglDWpnkNALE9ZX/PoVdUF8APDGD74RAxsHgjuBGGYwjdCu/vCt7GiNz4HmdUsqSDEKoQKAiN4J4ClmPupdrFmVQ34L2mZxAfNeZh5k5sELLrggrHm5wGYq2r+2P3iU1qDHifsSmF6GUm+pmpZ496rdGKZhfKr7Uxim2hGz2yFvuGsDiqXFh9pNdxBF3+pMGM3fvUTNn+SN1tV56tiqBkyzEFedsmbPGqNQLp5dxNgtY3UxHAAweXgSQEgHz+kYXdOIwo2DVna0NjYsL0kEKUbBZnh2FYBfJqK1AM4CcC6cGcF5RNStRvkXA3hcrX8SwCUAThJRN4AygNOe5S7ebToam6mo2zGUV8bnY+59CUw+35e/53LctOKmGvuA1+NlZNMIRq4dqY54TQUwbN0uR3eM1unOg6pGRYm69QbC7V612+wOaXl9w3zDpx6c0gsZU3ZULD4LAxv1FbSAcJVcK2lHf/hWB3J5r8lwYThwXe9AK20XUMBCADDzjQBuBAAieiuA/8zMG4noywB+BcDdADYDuE9tcr/6/g/q968zMxPR/QD+kog+B+A1APoBfDPe02lPSstLoTnD3Y5haNcQRjaNNO1j7o8KtknVq0W1w7b9YUTV19rut9RbwvZntltvd9OKmxy7iypaHsXv3mXdzevQd1XfYoI9m3gOT5bPNXvWZCJjZB5ISnAFBQH6q7yl7QIKNJcL6GMAPkJEx+Ho+G9Ty28D0KuWfwTAxwGAmY8BuAfADwD8FYAPM3NjbhI5YmL/BF55/pXQ9dyRw8DGgcBOxNZQPFeZq1vmNwxOHp6MLS+Qrb41qr7War/kCCivyipsu8qpCsDOf/dzI+6DNQn2LIW2tzNIW0UgRMNkEyj1lmruXRZcQAGAmLObe35wcJDHxsbSbkZLMQXlePHn2DcG8qi0zi5h6Z396/sZLgzHVhFsw136wCX/NFg369DVGPBuH2lGpEbhpd4Szjx3pqHI57Dr5sfmHsdxHCEb2Kh2jO8WATsXdjbdBiI6yszmVLsKiQROmUBVBCFS+Tmdp8zAxgHjw2bjahiHvcE1JPvRTYPHbhlD8WzHi0andtG9XFHTZADhKqsgoroPNnoNs+5fL+ixUTcFpUDRFQlqFZIOOmWMqo2VZevo2TDVQKPqk6YDruAIJpP7qsmAO/vSLOYqc9VaASObRrB71W4c2npIG8zWqmyfJvyGvKA4gmp6jQC6ztLHQ2bdv15oHNO7xfMcW5SyDaICShldJtAglUfSx5jYP4EDmw9YqUp6lvVg5sWZujTM/pG8deUxv9HUYEQt9YYb0ePCe91srmuQ+sc1xAP6LJ+i7883Qe9Ws+o/UQG1CUnkGm/mGAMbBxwduwWl3hJufOFGAGYvh6kHp8I9i1z874VBBrlR0q0WAlQga0OeTeI6r1cSgBq3z6gBdELjpOWOGfRuJaX+k6esQ2jGDc7WFuB9aE2d49G9R2NPOe2+tMac91HTaRtg5ppraOOaanLx1fnyez2z3AA6IPuZNtuZtN0xkyi6E4TYAFImaqbCRnPXNEP/2v5QPTZgV+jFuvM3Hc+33DV+B0XmbrgzuISkLW5MgEuYbcXk4uvm+/HeS1OupgPvO5Dove400nbHTDsdhMwAUiJID+5P0mZykUxitDKxfwLj+8brRtCF7gIW5hZTGUQp9KLL+FnqLaFnWU+oO+jqzasxeXhSO133z3LcaxxUPjIKMy/M1Hho9K/tx9gt9TaqyqkKhgvDzrlqBF7POT0AavX+JsEYVBchC5Gk7U7aGTnTLjcpRuAUsC0BWVfX16DKaKW/uMmI6e+w/Q+t7hwLxQJ4ges6u66eLqy/fb229KPti2ETT1AH2UVhe/Fe60b9+1333mZiA5JwHugEbGNq2g0xAmcYm/w11EX161iW8osT074rpyt1RkwvupHNzIsz2s6255webadla7fQxhPYlGRkaNMtBOFej4n9Ew3795f7yg3fM/eYNgZoIZyk6h5nFREAMRM0arV1f6wb+YfQSoORaYTs14fr8HfgpkRZldPNee9oBarFxLa8slxtn62ra7mvXBU4jeCmtrZyg9Xg2jYaVV2I2qiWpFUwWbv+IgBiJMijAKj39TbRXep2Cn/rVBM+NVA7jVZa5fHQyGjae91sXV3dbaJkIPXjprbWjTwLxQKIKLCSmCukGrmWaXu8ZJWkEsVl8fqLF1CMBE3Lo3QalVMVvPL8K3U1ZYtLixi8fjDR5GCm0Xkjo/ZWeTwYOz2/J5H6rrtuYULIu00zKjdvamtvNHept4Ql5y5xOv8AjyvXy6mRa5m2x0unk8XrLzOAGDFOy91UwBFYmF0INbQmQZyj9lZNt0163CCPId0+TN5Cpd5SjUGw2RxJ3pz/2ohikybKUwmskWuZtsdLp2Nz/ZNWEYkAiBFjx0BKrx9QCERHmKE1CeI2krVium3TGVZdQjeNoNxXxvLLluPEN06A5xnURbhyy5XG/buppCN5GAXgF57Ws0NfJbCo1zLtoKNOJ+z6p6EiEhVQjAztGjIWvtTl3w8jCy9mFsrW2eCvZaBzSfUG2z0y+khVn87zjLFbxtCzrEe/c0LNtmO3jmH25dmqQbbUW6pT15nwC88o3kTNVgJLO+io0wm7/mmoiGQGECMDGweMagRd8FMQWXox27EMoBfbEfbMSzN2sRfqO89zTbZTY+UvTw0CwMluOrpjtDqTsCGO5yHtoKNOJ+z6p6GiEwFgQRS9nKlmr5sh0wZ/ucZORRfcZavTr9neVl/PTgUu7zHDtnVHaK6NwD2ee7/LK8vVjjtKrEKhWMCSc5c0VIoyiHYX5u1O0PVPQ0UnAiCEqHq5IIOkSW/spkdwOwt5Qc3FYlzC7oNttLUOr8HXJtp3emq67nju7MC9n7tX7Y4Uq3D1HVfLc9BhpBGUJjaAEKLq5Uw683U3r6tJVua1FfBCbWch2Kltgu5Do776/v3ZFMUp95VDn5Mo03hvgFoapJFwUEjH3iYzgBAa0cuZpnlBUacSxl+LbYfZyP0Jqh3g365Gb6vR77uCOyyvu23OobRtP1kMVuokklbRiQAIIapeLiwVxMEtB422APHHXsTW1z4oJbNu++LZxcAgttLyUlXt49fj11Qz891fk63BSn+rhEp5pWPn8LqrJj0rlBxD6ZNkLIAIgBCi6OV0o6eRa0dw5IYjWLNnTahaIgtun63G9uEOLPCiCBotm1ItBMViFIoFzLywmLDOFdT+UXAU24/bvsDIaXacBPyG4TRG3xIsli5Jz8DEBhBCFL2cqYN3qzsFjWjTnvonQZTiN7rrPvghfRoMnc5at/2Sc5cEtq+aikHD7MuzOLD5gFEfHvachAn36qzQNzlMOlVAWJEbobUkHQsg9QBiZLgwHOjZYXIFpS7Cu/e92+jNkhe/7VbkXo+SFz/o/tjWFG40534zXkkgYOfCzujbNYDUGUgX4zMa8RmwrQcgM4AYsRnl6SIBgzr/KOUis04r1AtRRkxB92fmhRmr4zU6GnNnCG4wWBSSHH23S+R3Xkl6BiYCIEbCXAbdl8n25cpi9sBmaMXDHUWo9K/t167bdVZXYApm22OGMbBxANuf2Y4Nd22oPgNuOgkTaagGg9JqCK0l6XQdYgQOIYoKxl1+8DcOao2NMy86o0xbdUfeDHKtCHSJ4qU1eXhSu4/5M/adP+AE7g0Xhmueh6jPid8zTJd+QgIDO4+k03WIDSCAKPrQmrQDhtq9AAACLn3bpTh9/HToDc5jvVLbjlK3HrD4YrgVySqnKlrf/Kg2AB3Fs1VeoIBtTFHeUVI55MnOIzj388gNR6o2pTRSu9jaAEQABGDqgKlLpW7o0+d5iUqQUOkkg1yQEA2tlmUxag4qcD9XmYtcnSsKeb5vwiIT+ydw73X3YmF2oWZ5V08X1t++PrH7L0bgGDCpWniea4yyR2440nDnD5j1+p1kkKsxeAN1o+6F2YXgzpgXZ0ZBRV90+tU1e9ZoXUbj6vyB9rbdCPaM7hit6/yBxUpwWUNsAAHYZoNspvN3MQmbTsne2EydXZcw20iYftWmgH0r2ye0P0H3OIv3XwRAADbRqHHR6YE2cbwcNtfQVqA2W/bRtE8h3wQ9N1m8/6ICCsCvgjG57JV6S/XunxFqAHdCFHAYYS9HoVgIrLoV9zW0yQIaBbnHnYHJ1bjQXcjk/ZcZQAhhLnveilBey39xaTFcbw1HqHj1w52g7tGhnW35DLuAxgso5oIpLu6+vPe0UXSGafH8yScmV+Ml5SWZvL8iAELwv6irN692qlKpbJFujpi69M6WBeBNCcc6DVv/5ySvjZvlsxkBQF1U57IrKZfzi0mVGZgMUJHGoEAEQAC6F3V833id37dtqccwOj3tbhYN3s3aJq7ccmXdMkm5nF8aLeuY1qBAbAABmF7Uo3uPtswwnEVPgU6mGcPd4IcGse7mdXXL8xbhLSzSaCqHtNK+hAoAIrqEiP6WiB4momNEdINavpyIHiCiSfX/fLWciOhPiOg4EX2PiK7w7GuzWn+SiDa37rTsCCt9FxgH0CJc3XZeabdyg7oXOswgDTiqn7Fbx7TnKCmX80ujsTtpDQpsVEBzAD7KzN8monMAHCWiBwC8H8AoM3+GiD4O4OMAPgZgDYB+9fczAG4B8DNEtBzATgCDcMJ8jhLR/cz8bNwnZYPNlKsVroCA0zl0n9VtbSfIC+2o+9bZJvrX9uPYPccCbQNBtp00in8LydGIKrNR1VGzhM4AmPkJZv62+vwCgIcBXARgPYB9arV9AK5Wn9cD+At2+EcA5xHRhQB+CcADzHxadfoPAHhHrGdjgTsCHbl2JHTK1b+2P5I7py28wEYVko2xqF1p1+ym3uyYQ7uGML5vvKbzLxQLTppng6uw/xw7KcJbsCPpLKAukYzARLQKwBsBPATg1cz8BOAICSJ6lVrtIgCPeTY7qZaZlvuPsQXAFgDo6+uL0rxQbIpyuFOuif0TGN83Hil5mC2uVE9D4qdJHnTfOiG2MLuAnmU92P7MdmMEsa7YvHT4gkvSWUBdrAUAES0D8L8AbGPm54mMQ2PdDxywvHYB814AewEnGZxt+2ywSTfgdsBxpCbQ0dXTZUwgl3c1gGmam4TdIy4XuzAhFsdUXmIEOpM0BgVWAoCIinA6//3MPKIW/5iILlSj/wsBPKWWnwRwiWfziwE8rpa/1bf8G403PTphI01vB9wK3T/gqAu8wUzdpe6WBTNljaFdQ9pMiTMvzFTr+LaCOG0PYR18s/r9drSTCM3jzYTrlo5Noh6EjRcQAbgNwMPM/DnPT/cDcD15NgO4z7P8fcob6M0AppWq6GsA3k5E5yuPoberZYkRNApz9bCAkza4aQwTpNmXZqslHiunKpirzGHDnRs6ovLSwMYBbWH2VmdKjNP2EKarbVa/3652EqFxDm09hJFNI9WBhd+BoJWecjYzgKsAbAIwQUTfVct+F8BnANxDRB8EMAXgV9VvhwGsBXAcwMsArgMAZj5NRH8A4FtqvU8x8+lYzsIS0+jMfUEPbT2EsVvHmtb7l3pL6FnWYzWL8AYAdcLU32TkbqUdIE7bg42utpmpfB7sJII9E/snAvucVgcIhgoAZv57mH1h6ua17FSY+bBhX7cDuD1KA+NkYOMAph6cwtG9R8HzDOoirN68utr5xtH5F4qFam4gnbpDx/TUdMdM/dNwd4v7mK3U1ablDiikw+iO0dA+p5XCP7eRwBP7J3DTipswTMMYpmHctOImHNp6COP7xqtTLJ5njO8br468m+38qYtw9R1XVzsInbpDR7mv3DFT/zTc3UyZPWdenMlcIFpa7oBCOth07q0U/rkUABP7J3Bg84EaX+3KqQrGbhnTdrJHbjgSi9GXF7hmZGjj0+++3J0y9U/DB949Zqm31tuocqrSch1rVCRGoLMI69xbLfxzmQzuyA1HIqVraDbdr4v/ZoZFElMXVV/uai3ckH3mgTTc3UyZPd0BQNLtCbL3SIxA5xBUdCoJL6BcCoC4OvQo6CT10K4hjGwaMaqWeJ6rKh5JD9B6jKl6T1Va6obqp1PsPUI4aQWAuZBjs80mg4ODPDY2Fnm7YYq/nmsQpd4S1uxZo71pNm1xPZGA9B6ETmD3qt3mcn2qoHya7UiyDUK+IaKjzDwYtl4uZwCl3lKis4C5ypzxt/JKu8LyoztGOyIWIE2Gdg1h5NoR7W9J2lo6xd4jZJ9cGoEvf8/liR4vyFvHtrasvPytZ2DjQJ0h2MWmYEdcaawlHbRgIul06bkTANUkbglj6sBtC8vLy58Ma/asiexm6ers3QjusAjNsJdYXD0FHVGfszjInQBoVRK3MII6cG864Xfve7e8/CnSiJtllBgNm5dYXD0FHWnEAuXOBhCXKqWazsGiCEiUDjxtq78Q3c0yis7ett6vuHoKftKwDeVOAMRRxau4tKj16nHruzabs0de/vYiShprMfAKjZJGGpDcCYCgwApbLn7LxXUddCckamsFebhuUdJYSy6f/NLqZzmNWKDc2QD8+tVGeGT0kRqdbRrGmTyQl+sWJY21GHjzSRLPchq2odwJAKDW6Noo3he7UxK1xU1ertvE/gljXImu1GNWDbxJuxjmiVY/y9Va5ZucOJWkaoTkTgUE1E7V3Oo6UZl+dLo6vRe9bmPk4bq5Iz8TOtVOFm08kn6iOVr5LKd5b3I3A/BP1Rrp/F3cKZ4E7jRGHq5bkFtxO6l28jIbS4tWPstp3pvcCYA44wDcmyB63cbIw3ULGuFlRbVjQx5mY2nSymfZ5LXYqrrkXnInAOJ+oKenpjOt180yebhuxpHfynI+zqONZmNp0spn2ZQdwLQ8TnJnA4gjDsC/P6B1et08uEkGkUV9eBTykqY7L+eRJq16lk1q6mbU17bkbgbQv7a/oe2oi9DV01WzrNUvSF7cJPOMrppYd6n9xk15mI3llfJK8yyz1bTfkxzC5OFJuxUJ1UItbj5/INkUDbZpA4TWEzYT86b8dktJAu3lQdPus7G8kubsLHcCIIoNQFdyLckXRAxz2SDMDU8EtdBK0swPljsBYG0D4Pj8bRvV40vagPhoxpYS1sGLoBZaTVqzs9zZAGwLsLg04287sX8CN624CSPXjjSkx8+Dm2QWaNaWEtbBiweNkFdyJwD8xq5Sb8kx4AV4VDUyknM7HV2KAFuhIoa5eGg2kCasgxdBLeSV3KmAAPN0ylgUnJ3fmlUbeLEVKmKYa55mVTRhRjip4SDklVwKABNBqaKj2gPCOhdRDyRHs7YUmw5eBLXQatKICeooAVDzoms6jCieHUHGZlEPJEscbnTSwQtpklZCuNzZAMJwU0WbsI0iNhmbS70l0eMnjNhShHYnrYRwHTUD8GJKE22bf0P0wtlCRvBCO5OWq3HHCoA48m9IpyMIQhykFRPUcSoglzTzbwiCIHhJy9W4YwWA+HYLgpAV0rJjdawKSHT4giBkiTRUyh0rAADR4QuC0Nl0rApIEASh00l8BkBE7wCwB0AXgC8w82eSboMgCEJU8li9L1EBQERdAP4UwC8COAngW0R0PzP/IMl2CIIgRCGtSN1Wk7QK6E0AjjPzj5h5BsDdANYn3AZBEIRIpBWp22qSFgAXAXjM8/2kWlaFiLYQ0RgRjT399NOJNk4QBEFHXosCJS0AdHkWakJvmXkvMw8y8+AFF1yQULMEQRDM5LUoUNIC4CSASzzfLwbweMJtEARBiEReA0eTFgDfAtBPRJcSUQ+AawDcn3AbBEEQIpHXjLOJegEx8xwR/SaAr8FxA72dmY8l2QZBEIRGyGPgaOJxAMx8GMDhpI8rCIIg1CKRwIIgCB2KCABBEIQORQSAIAhChyICQBAEoUMhZvsSiElDRE8DeLSJXawA8ExMzWkV7dBGQNoZJ+3QRkDaGSdJt3ElM4dG0mZaADQLEY0x82Da7QiiHdoISDvjpB3aCEg74ySrbRoLJfQAAAR6SURBVBQVkCAIQociAkAQBKFDybsA2Jt2AyxohzYC0s44aYc2AtLOOMlkG3NtAxAEQRDM5H0GIAiCIBgQASAIgtCh5FIAENE7iOiHRHSciD6ecltuJ6KniOj7nmXLiegBIppU/89Xy4mI/kS1+3tEdEVCbbyEiP6WiB4momNEdENG23kWEX2TiMZVO4fV8kuJ6CHVzi+pVOMgoiXq+3H1+6ok2qmO3UVE3yGir2a4jSeIaIKIvktEY2pZpu65OvZ5RPQVIvon9Yy+JWvtJKLXqevo/j1PRNuy1s46mDlXf3DSTP8zgNcC6AEwDuD1Kbbn5wBcAeD7nmU3Afi4+vxxAJ9Vn9cCOAKnctqbATyUUBsvBHCF+nwOgP8H4PUZbCcBWKY+FwE8pI5/D4Br1PJbAXxIfd4K4Fb1+RoAX0rwvn8EwF8C+Kr6nsU2ngCwwrcsU/dcHXsfgF9Xn3sAnJfFdnra2wXgSQArs9xOZs6lAHgLgK95vt8I4MaU27TKJwB+COBC9flCAD9Un/8MwK/p1ku4vfcB+MUstxPAUgDfBvAzcCIsu/33H07dibeoz91qPUqgbRcDGAXwNgBfVS95ptqojqcTAJm65wDOBfCI/5pkrZ2+tr0dwINZbycz51IFFFp4PgO8mpmfAAD1/1VqeeptVyqIN8IZXWeunUq18l0ATwF4AM5s7zlmntO0pdpO9fs0gN4EmrkbwHYAC+p7bwbbCDj1uP+aiI4S0Ra1LGv3/LUAngZwh1KpfYGIzs5gO71cA+CL6nOW25lLARBaeD7DpNp2IloG4H8B2MbMzwetqlmWSDuZeZ6Z3wBnlP0mAD8V0JbE20lE7wTwFDMf9S4OaEea9/wqZr4CwBoAHyainwtYN612dsNRod7CzG8E8BIcVYqJtN+hHgC/DODLYatqliXeT+VRALRD4fkfE9GFAKD+P6WWp9Z2IirC6fz3M/NIVtvpwszPAfgGHP3peUTkVrfztqXaTvV7GcDpFjftKgC/TEQnANwNRw20O2NtBAAw8+Pq/1MADsARqFm75ycBnGTmh9T3r8ARCFlrp8saAN9m5h+r71ltJ4B8CoB2KDx/P4DN6vNmODp3d/n7lIfAmwFMu9PHVkJEBOA2AA8z8+cy3M4LiOg89bkE4BcAPAzgbwH8iqGdbvt/BcDXWSlcWwUz38jMFzPzKjjP3teZeWOW2ggARHQ2EZ3jfoajt/4+MnbPmflJAI8R0evUoiEAP8haOz38GhbVP257sthOh6SNDgkZYdbC8WT5ZwA7Um7LFwE8AWAWjtT/IBwd7yiASfV/uVqXAPypavcEgMGE2vjv4Ew/vwfgu+pvbQbb+W8AfEe18/sAPqmWvxbANwEchzP1XqKWn6W+H1e/vzbhe/9WLHoBZaqNqj3j6u+Y+55k7Z6rY78BwJi67/cCOD+j7VwK4BSAsmdZ5trp/ZNUEIIgCB1KHlVAgiAIggUiAARBEDoUEQCCIAgdiggAQRCEDkUEgCAIQociAkAQBKFDEQEgCILQofx/lIKtiVv3fMsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0c02dc5110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]),data[\"cnt\"].values,color='purple')\n",
    "plt.title(\"Distribution of number of people\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0c02d257d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = data[data.cnt>0]\n",
    "plt.scatter(range(data.shape[0]),data[\"cnt\"].values,color='purple')\n",
    "plt.title(\"Distribution of number of people\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 16)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = data.columns\n",
    "data_corr = data.corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0c0a9f2290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(12,9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "sns.heatmap(data_corr,mask=data_corr<1,cbar=False)\n",
    "\n",
    "plt.savefig('bicycle_coor.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 丢弃相关度小的特征和不需要预测的列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('holiday', axis = 1).drop('weekday', axis = 1).drop('workingday', axis = 1).drop('registered', axis = 1).drop('casual', axis = 1).drop('dteday',axis=1)\n",
    "X.shape\n",
    "columns = X.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant  season  yr  mnth  weathersit      temp     atemp       hum  \\\n",
       "0        1       1   0     1           2  0.344167  0.363625  0.805833   \n",
       "1        2       1   0     1           2  0.363478  0.353739  0.696087   \n",
       "2        3       1   0     1           1  0.196364  0.189405  0.437273   \n",
       "3        4       1   0     1           1  0.200000  0.212122  0.590435   \n",
       "4        5       1   0     1           1  0.226957  0.229270  0.436957   \n",
       "\n",
       "   windspeed  \n",
       "0   0.160446  \n",
       "1   0.248539  \n",
       "2   0.248309  \n",
       "3   0.160296  \n",
       "4   0.186900  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = X[X.yr == 0]\n",
    "test = X[X.yr == 1]\n",
    "y_train = train['cnt'].values\n",
    "y_test = test['cnt'].values\n",
    "X_train = train.drop('cnt' , axis=1)\n",
    "X_test = test.drop('cnt' , axis=1)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征分离处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "X_train_label= X_train.loc[0:,['season','yr','mnth','weathersit']]\n",
    "X_test_label= X_test.loc[0:,['season','yr','mnth','weathersit']]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pxt/.local/lib/python2.7/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X_train_num= X_train.loc[0:,['temp','atemp','hum','windspeed']]\n",
    "X_test_num= X_test.loc[0:,['temp','atemp','hum','windspeed']]\n",
    "\n",
    "ss_X = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "\n",
    "X_trainset = ss_X.fit_transform(X_train_num)\n",
    "X_testset = ss_X.transform(X_test_num)\n",
    "\n",
    "\n",
    "y_train = ss_y.fit_transform(y_train.reshape(-1, 1))\n",
    "y_test = ss_y.transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.          0.          1.         ... -0.61214601  1.09174727\n",
      "  -0.40316146]\n",
      " [ 1.          0.          1.         ... -0.6707804   0.3529164\n",
      "   0.74411127]\n",
      " [ 1.          0.          1.         ... -1.64545409 -1.38946848\n",
      "   0.74111588]\n",
      " ...\n",
      " [ 1.          0.         12.         ... -1.20366773 -0.46787213\n",
      "  -0.93756476]\n",
      " [ 1.          0.         12.         ... -0.87793429 -0.04711029\n",
      "  -0.74319011]\n",
      " [ 1.          0.         12.         ... -0.31265155 -0.18736873\n",
      "   0.3744413 ]]\n"
     ]
    }
   ],
   "source": [
    "X_train = np.hstack((X_train_label,X_trainset))\n",
    "X_test = np.hstack((X_test_label,X_testset))\n",
    "print X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is -0.709475655879968\n",
      "The r2 score of RidgeCV on train is 0.7546366102228651\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "\n",
    "\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "\n",
    "ridge.fit(X_train, y_train)    \n",
    "\n",
    "\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "\n",
    "\n",
    "print 'The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge)\n",
    "print 'The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2ioj9wJf7sZtmZv1mzQgv0JhlZF+TZmY2wKqqa5k/fQKVZ47MAo1ZHCRmZv1kZ0MjL729lxsqzxixBRqz9DpIJH1B0p+k0xWS5uXXLTOz4WfV+jqKi8TXl3R3y9zI06sgkfQ94LvAv09njQH+R16dMjMbblrb2nlsfR2Xn1vBjBFcoDFLb49IvgosAw4BRMQuYOTfZWNm1ku/fqOB+oMjv0Bjlt4GSUtEBOmd6ZIm5NclM7Php6NA4xXnjewCjVl6GyRVkh4Epkj6M+CfOHZfh5nZqNZwsJnnXq/na0tmj/gCjVl6dR9JRPzfkq4CDgDnAvd2VOY1MxvtnthYlxZoHLlPQexOr4IkHcp6Ln02yLnAuZLGRMSRfLtnZja0JQUa61gyZwpnzxidp457ewz2PFAmaRbJsNafAP89r06ZmQ0XG97dz/b6xlF1J3tnvQ0SpQ+W+hrwVxHxVWBhft0yMxseVtXUMr60mC8vOn2wuzJoeh0kkj4L/BHwi3RernW6zMyGukPNrfxs0y6+fMFMystG76/E3gbJXwB3A4+nFXznAc/l1y0zs6Fvzau7OdTSNqqHtaD3RxVNQDtwc/o0Q9HF0w7NzEaLqppa5ldM4NOjqEBjlt4GyU9IHnv7GkmgmJmNajsaGql+ex93X3PeqCrQmKW3Q1sNEfGziHgrIt7p+OppJUlXS9omabukuzOW3yVpi6RXJK2VdGbBsjmSnpG0NW0zt9O6fyWpsZf9NzPrV6tqkgKNXxtlBRqz9PaI5HuS/g5YC3Q8V52IeLyrFSQVAw8AV5E8Z71a0uqI2FLQbCNQGRFNkr4F3A8sT5f9GPh+eu9KOQVHQpIqgSm97LuZWb9qbWvnsQ11XH7uDGZMHF0FGrP0Nkj+BDiPpOpvxy/0ALoMEuASYHtE7ASQ9AhwHceevU5ErCto/wJwS9p2IVDScfd8RBw98kgDagXwhyTFJM3MBtSvtjXQcLB51N7J3llvg+TCiLjgBLc9C6gteF0H/G437W8DnkqnzwH2S3ocmEdyE+TdEdEG3Amsjojdo31c0swGx8qaWqaXl3H5KCzQmKW350heSI8STkTWb/nMK73SK8EqSY40IAm4S0lO8F8MzAdulXQ6cAPwVz2+uXSHpBpJNQ0NDSfYdTOzbPUHD/Pc6/V8fcmsUVmgMUtvP4UvAC+nJ85fkfSqpFd6WKcOKLy4ejawq3MjSUuBe4BlEdFcsO7GiNgZEa3Ak8ASYDFwNrBd0tvAeEnbs948Ih6KiMqIqKyoqOjlbpqZde+JDe/R1h6j8rkjXent0NbVJ7HtamBBevPie8BNJOc1jpK0GHgQuDoi6jute4qkiohoAK4AaiLiF8BpBes3RsTZJ9E3M7MTlhRorOXTZ57C2TPKB7s7Q0Zvy8j3eKlvxjqtku4EngaKgYfTu+LvIwmF1SRDWeXAqvR8x7sRsSwi2iR9B1irZMF6/PwTMxtkG97dx46GQ9z/9bMGuytDSq7FYSJiDbCm07x7C6aXdrPus8CiHrbvPwnMbMBUVdelBRpnDnZXhhSfKTIz64VDza38/JVdfGXRTCaM4gKNWRwkZma98AsXaOySg8TMrBeqqpMCjUvmjO4CjVkcJGZmPdhe30jNO/tYXnnGqC/QmMVBYmbWg1XraykuEl91gcZMDhIzs24caWvnsfXvccV5LtDYFQeJmVk3frWtgT2NzdzoO9m75CAxM+vGyupaKiaWcfm5LrXUFQeJmVkX6g8eZt22er62ZBYlLtDYJX8yZmZdeDwt0Ohhre45SMzMMnQUaKw88xTOqnA1pu44SMzMMqx/Zx87Gw5xo+9k75GDxMwsQ1VNLRNKi/nyBS7Q2BMHiZlZJ43Nrfz8ld18ZdHpLtDYCw4SM7NOfvHKLppa2jys1Uu5Bomkq9PH826XdHfG8rskbUkf37tW0pkFy+ZIekbS1rTN3HT+T9JtvibpYUlj8twHMxt9qmrqOKtiAkvmTBnsrgwLuQWJpGLgAeAaYCFws6SFnZptBCojYhHwKHB/wbIfAysi4nzgEqDjUbw/Ac4DLgDGAbfntQ9mNvpsrz/I+nf2sfxiF2jsrTyPSC4BtkfEzohoAR4BritsEBHrIqIpffkCMBsgDZyS9CmJRERjR7uIWBMp4KWOdczM+sOqmjpKisRXF/tXS2/lGSSzgNqC13XpvK7cBjyVTp8D7Jf0uKSNklakRzhHpUNafwz8sh/7bGaj2JG2dh7bUMcV582gYmLZYHdn2MgzSLKOCSOzoXQLUAmsSGeVAJcC3wEuBuYDt3Za7a+B5yPiN11s8w5JNZJqGhoaTrz3ZjbqrHu9nj2NLb6T/QTlGSR1QOG/xmxgV+dGkpYC9wDLIqK5YN2N6bBYK/AksKRgne8BFcBdXb15RDwUEZURUVlR4WJrZtazqpqkQOOXXKDxhOQZJNXAAknzJJUCNwGrCxtIWgw8SBIi9Z3WPUVSx7/mFcCWdJ3bgd8Hbo6I9hz7b2ajSP2Bw6zb1sDXl8x2gcYTlNunlR5J3Ak8DWwFqiJis6T7JC1Lm60AyoFVkl6WtDpdt41kWGutpFdJhsl+lK7zt8CpwL+k69yb1z6Y2ejx2NECjT7JfqJyvWUzItYAazrNu7dgemk36z4LLMqY79tMzaxfRQSramq5ZO5U5rtA4wnz8ZuZjXo17+xj555D3OCjkZPiIDGzUa+qOi3QuMgFGk+Gg8TMRrXG5lZ+8epu/uDC0xlf6pHzk+EgMbNR7eebXKCxrxwkZjaqVdXUcvaMchaf4QKNJ8tBYmaj1vb6g2x4dz/LK12gsS8cJGY2alV1FGhc0l0ZQOuJg8TMRqUjbe08vqGOK8+fwfRyF2jsCweJmY1Kz7lAY79xkJjZqFRVXcuMiWVcdo4LNPaVg8TMRp0PDhxm3bZ6vv5pF2jsD/4EzWzUeWxDHe2Bh7X6iYPEzEaVpEBjHZfMm8q86RMGuzsjgoPEzEaV6rf38daeQz4a6UcOEjMbVVZW11JeVsK1F5w22F0ZMXINEklXS9omabukuzOW3yVpi6RXJK2VdGbBsjmSnpG0NW0zN50/T9KLkt6UtDJ9+qKZWY8OHj7Cmld38wcXznSBxn6UW5BIKgYeAK4BFgI3S1rYqdlGoDIiFgGPAvcXLPsxsCIizgcuAToexftD4D9FxAJgH3BbXvtgZiPLz1/ZzcdH2jys1c/yPCK5BNgeETsjogV4BLiusEFErIuIpvTlC8BsgDRwStKnJBIRjRHRpKQYzhUkoQPwD8D1Oe6DmY0gVTW1LJhRzkUu0Niv8gySWUBtweu6dF5XbgOeSqfPAfZLelzSRkkr0iOcacD+9HnwvdmmmRkAb35wkI3v7mf5xS7Q2N/yDJKsf6nIbCjdAlQCK9JZJcClwHeAi4H5wK0nuM07JNVIqmloaDixnpvZiFNVU0tJkbh+sf/27G95BkkdUDgQORvY1bmRpKXAPcCyiGguWHdjOizWCjwJLAH2AFMklXS3TYCIeCgiKiOisqLCJRDMRrOW1nYe3/AeS88/1QUac5BnkFQDC9KrrEqBm4DVhQ0kLQYeJAmR+k7rniKpIwGuALZERADrgG+k878J/DTHfTCzEeC51+v58FALN148e7C7MiLlFiTpkcSdwNPAVqAqIjZLuk/SsrTZCqAcWCXpZUmr03XbSIa11kp6lWRI60fpOt8F7pK0neScyd/ntQ9mNjJU1dRy6qQyvrjAoxN5yPVC6ohYA6zpNO/eguml3az7LLAoY/5OkivCzMx69MGBw/xqWz1/ftlZLtCYE3+qZjaiPbreBRrz5iAxsxErKdBYy+/Om8pcF2jMjYPEzEasl97ay9sfNvloJGcOEjMbsVbWdBRonDnYXRnRHCRmNiIdK9B4OuNKiwe7OyOag8TMRqSfbdrN4SPtLL/Yw1p5c5CY2YhUVVPLOaeWc+HsyYPdlRHPQWJmI84bHxzk5dr93FjpAo0DwUFiZiNOVXUtY4rFV12gcUA4SMxsRGlpbefxjUmBxmku0DggHCRmNqI89/oH7D3U4ntHBpCDxMxGlJXVtZw2aSxfPMcFGgeKg8TMRoz3PzrMr99o4Bufnk1xkU+yDxQHiZmNGI9tSAo03lDp544MpFzLyA93Oxoa+biljbFjiikrKaJsTBFlJcWMHVNEaXGRLys0G0La24Oqmlo+M38qZ05zgcaB5CDpxv/58y2s29b1897LSoqOC5mxJcXHhU1ZSfHxbY5r3+n7ce0K1h/zyfVLiuQQM+vkpbf38s6HTfzFlQsGuyujTq5BIulq4P8FioG/i4gfdFp+F3A70Ao0AH8aEe+ky9qAV9Om70bEsnT+lSRPViwCGoFbI2J7Hv3/i6XncNMlc2hubefwkTaaW9tp7vy9cFlrG4ePJN+bj7Tz0cdHaD6S3aYvisQnwqbLIDoutHoXdmVjul7mBwPZUFVVXcvEshKu+ZQLNA603IJEUjHwAHAVUAdUS1odEVsKmm0EKiOiSdK3gPuB5emyjyPiooxN/w1wXURslfSvgP8A3JrHPlx0xpQ8NktE0NJWEDAZYdPcEUjHhVinsOq07HC6XlNLK3sPFbY5Nt3S2rcQKynSsbDJOLKaMm4MZ80o56yKcs6qmMD8inImjxvTT5+cWbYDh4+w5rXdfG3JbBdoHAR5HpFcAmxPH42LpEeA64CjQRIR6wravwDc0ovtBjApnZ4M7OqX3g4gSelf+8VMGjuwv2Tb29MQKwiezuHU+cjqcLdHX8nR2eH0+46GRp57vZ7W9jj6nhUTyzirYkIaLuWcNaOcs2eUM3PSWIp8ZY31g59t2pUUaPS9I4MizyCZBdQWvK4Dfreb9rcBTxW8HiuphmTY6wcR8WQ6/3ZgjaSPgQPAZ7I2JukO4A6AOXPmnNQOjERFRWJsUTFjxxQzmXxC7EhbO7V7m9jRcIjt9Y3saEi+Vm/axcHDrUfbjRtTzPzjAiaZnjd9AmPH+K9K672qmjrOPXUii1ygcVDkGSRZf2pGxjwk3QJUApcVzJ4TEbskzQeek/RqROwAvg1cGxEvSvq3wF+ShMvxbxTxEPAQQGVlZeb7Wj7GFBcxv6Kc+RXlXLXw1KPzI4I9jS1Hg2VH/SF2NDSy4d19rN507MBSgtmnjDsaMGcXDJVNnVDqCw3sONveP8im2v38719Z6J+NQZJnkNQBhceZs8kYhpK0FLgHuCwimjvmR8Su9PtOSb8CFks6AFwYES+mzVYCv8yn+9bfJFExsYyKiWV8Zv6045Z93NLGW3uSYDl2FHOIf9nxIc0F53WmjB9zNFQKh8rOOGWcLwQYpapqXKBxsOUZJNXAAknzgPeAm4A/LGwgaTHwIHB1RNQXzD8FaIqIZknTgc+TnIjfB0yWdE5EvEFyIn9rjvtgA2RcaTELT5/EwtMnHTe/vT14b//HR4MlOZJp5LnXG6iqqTvarrS4iLnTx39imGx+RTnlZb7KfaRqaW3niY3vcdXCU5k6oXSwuzNq5fY/LCJaJd0JPE1y+e/DEbFZ0n1ATUSsJrmMtxxYlR6Sdlzmez7woKR2kst8f9BxtZekPwMeS5ftA/40r32wwVdUJM6YOp4zpo7nS+cev+yjpiPs2JMEy/Z0qGzb+wd5ZssHtBWc7D9t0tijwVI4VHbqpDIPhQxza7cmBRpv8En2QaWIkX/6oLKyMmpqaga7GzZAWlrbeXfvIban52COHs3UN9LYfOxk/4TS4uMuVe4YJjtz2njKSnyyfzi49b+9xLb3D/Lb717h2lo5kLQ+Iip7audjfhtxSkuKOHvGRM6eMfG4+RFBw8Hm9Ojl2FDZizs/5ImN7x1tVySYM3X88Sf60yOaKeM9fDJU7P7oY55/o4H/9fKzHSKDzEFio4YkZkway4xJY/ncWdOPW3aoufXoyf7CkPnN9j3H3cQ5bULpccHS8TXrlHH+ZTbAHlufFmj8tIe1BpuDxAyYUFbCp2ZN5lOzjr8Poa09eG/fx2xvOHj0cuUdDY08vfkD9h46dptUWUkR86ZP+MRQ2fyKCYwv9X+z/pYUaKzjs/OnMWfa+MHuzqjnn3CzbhQXiTnTxjNn2niuOO/4ZXsPtbCz0zmYze99xFOv7qbgXD+zpow7duPljHLlfe+OAAALpElEQVTOTo9oKsp9sv9kvfjWXt7d28S3r3KBxqHAQWJ2kqZOKGXqhKlUzp163Pzm1jbe3tNUMEyWBE1VTS1NLW1H200cW8LcaROSe2vKy5g+sTT9Xsb08uR+m+nlZUwaW+LA6aSqppaJY12gcahwkJj1s7KSYs49bSLnnvbJk/3vHzh83BDZOx828cGBw2ze9RF7GluOu2y5Q2lJ0dGAqSgvPRowWd8nlBaP+NA5cPgIa17dzTc+PduldIYIB4nZAJHEzMnjmDl5HF9YMP0Ty9vbg/0fH6HhYDN7GpuP+96Qfn9v/2Ferv2IvYeaycgcxo4pOhYsR8Pn2PeKiaVUlI9l+sTSYXvuZvXLu2hubWf5xT7JPlQMz58ksxGoqEjpcFkp5zKx27Zt7cHeQy2fCJxj31t458Mm1r+zj71NLWTdLjahtPhY0BwdWht73BBbRXqkM5T+8l9VU8t5p03kglku0DhUOEjMhqHiomN1y87v4TRBa1s7ew+1UN8paAqDZ0dDIy+81cz+piOZ25hYVlJwdFN6NHyOG16bWMb08tJcb+Z8/f0DbKr7iHtdoHFIcZCYjXAlxUVH75/pSUtrOx8eambPwRYaGg+n348Nr+052Mzr7x/ktwf3cKDgkQCFJo0tyTx/U9HpqGfahDJKS06s0GZVdR1jisX1LtA4pDhIzOyo0pKio+dxkufGde3wkTY+PJQe2RQEzZ7GjukWNu86wJ6DzRxszg6dKePHZB/dFFxUMGNiGVMnlNIWwRMb6/i9hae5QOMQ4yAxs5Mydkwxs6aMY9aUcT22PXyk7bijmo6gKTzq2VS3n4aDzcddIt1BSobXDhxu5YbK2XnsjvWBg8TMcjd2TPHRKs49OdTcyp7GwivWjp3PGTemmEsXVAxAj+1EOEjMbEiZUFbChLISzpw2YbC7Yr3kR8qZmVmf5Bokkq6WtE3Sdkl3Zyy/S9IWSa9IWivpzIJlbZJeTr9WF8yXpO9LekPSVkn/Os99MDOz7uU2tCWpGHiA5HG4dUC1pNUdTzpMbQQqI6JJ0rdIHqe7PF32cURclLHpW0meBX9eRLRLmpHXPpiZWc/yPCK5BNgeETsjogV4BLiusEFErIuIpvTlC0BvLsf4FnBfRLSn26jvob2ZmeUozyCZBdQWvK5L53XlNuCpgtdjJdVIekHS9QXzzwKWp8uekuQ60mZmgyjPq7ay6hdkPiBe0i1AJXBZwew5EbFL0nzgOUmvRsQOoAw4HBGVkr4GPAxcmrHNO4A7AObMmdO3PTEzsy7leURSR3Iuo8NsYFfnRpKWAvcAyyKiuWN+ROxKv+8EfgUsLtjuY+n0E8CirDePiIciojIiKisqfN25mVle8gySamCBpHmSSoGbgNWFDSQtBh4kCZH6gvmnSCpLp6cDnwc6TtI/CVyRTl8GvJHjPpiZWQ8UWfWl+2vj0rXAfwaKgYcj4vuS7gNqImK1pH8CLgB2p6u8GxHLJH2OJGDaScLuP0fE36fbnAL8BJgDNAJ/HhGbeuhHA/DOSe7GdGDPSa6bJ/frxLhfJ8b9OjEjtV9nRkSPQzq5BslIIKkmIioHux+duV8nxv06Me7XiRnt/fKd7WZm1icOEjMz6xMHSc8eGuwOdMH9OjHu14lxv07MqO6Xz5GYmVmf+IjEzMz6xEHSiaQVkl5PKxI/kV5unNWu28rGOfTrBkmbJbVL6vIqDElvS3o1rZpcM4T6NdCf11RJz0p6M/1+ShftMqtM59Cfniphl0lamS5/UdLcvPpygv26VVJDwWd0+wD162FJ9ZJe62K5JP2XtN+vSFoyBPr0JUkfFXxW9+bdp/R9z5C0Lq2GvlnSX2S0yffzigh/FXwBvweUpNM/BH6Y0aYY2AHMB0qBTcDCnPt1PnAuyV3+ld20exuYPoCfV4/9GqTP637g7nT67qx/x3RZ4wB8Rj3uP/CvgL9Np28CVg6Rft0K/NeB+nkqeN8vAkuA17pYfi1JbT4BnwFeHAJ9+hLw80H4rGYCS9LpiSQ3aXf+d8z18/IRSScR8UxEtKYvu6pI3GNl4xz6tTUituX5Hiejl/0a8M8r3f4/pNP/AFzfTdu89Wb/C/v7KHClpKx6dQPdr0EREc8De7tpch3w40i8AEyRNHOQ+zQoImJ3RGxIpw8CW/lkgdxcPy8HSff+lOMrEnc40crGAymAZyStTwtXDgWD8XmdGhG7IfmPBnT13Jquqkz3p97s/9E26R8yHwHTcurPifQL4OvpcMijks7IWD4Yhur/wc9K2pRWJv+dgX7zdEh0MfBip0W5fl6j8pntaWmW0zIW3RMRP03b3AO0kpRj+cQmMub1+fK33vSrFz4fSdXkGcCzkl5P/5IazH4N+Od1Apvpqsp0f+rN/ufyGfWgN+/5M+AfI6JZ0p+THDVd8cnVBtxgfF492UBSUqQxLQ/1JDBgj7mQVE5S0PbfRMSBzoszVum3z2tUBklELO1uuaRvAl8Brox0gLGTXlU27u9+9XIbHVWT6yU9QTJ80acg6Yd+DfjnJekDSTMjYnd6CJ/5ALSCz2unpF+R/DXX30HSm/3vaFMnqQSYTP7DKD32KyI+LHj5I5LzhkNBLj9TfVH4yzsi1kj6a0nTIyL3GlySxpCEyE8i4vGMJrl+Xh7a6kTS1cB3SSoSN3XRrMfKxoNB0gRJEzumSS4cyLzCZIANxue1GvhmOv1N4BNHTuq+ynR/6s3+F/b3G8BzXfwRM6D96jSOvoxk/H0oWA38L+nVSJ8BPuoYyhwskk7rOK8l6RKS368fdr9Wv7yvgL8HtkbEX3bRLN/Pa6CvMBjqX8B2krHEl9OvjitpTgfWFLS7luTqiB0kQzx59+urJH9VNAMfAE937hfJ1Teb0q/NQ6Vfg/R5TQPWAm+m36em8yuBv0unPwe8mn5erwK35difT+w/cB/JHywAY4FV6c/fS8D8vD+jXvbr/0p/ljYB64DzBqhf/0hSFfxI+vN1G/DnJNW+IRmqeSDt96t0cyXjAPbpzoLP6gXgcwP0WX2BZJjqlYLfW9cO5OflO9vNzKxPPLRlZmZ94iAxM7M+cZCYmVmfOEjMzKxPHCRmZtYnDhKzbkhq7OP6j6Z3zXfX5lfqpnJyb9t0al8h6Ze9bW/WFw4Ss5yktZaKI2LnQL93RDQAuyV9fqDf20YfB4lZL6R3BK+Q9JqS570sT+cXpaUwNkv6uaQ1kr6RrvZHFNxRL+lv0gKRmyX9xy7ep1HS/yNpg6S1kioKFt8g6SVJb0i6NG0/V9Jv0vYbJH2uoP2TaR/McuUgMeudrwEXARcCS4EVafmQrwFzgQuA24HPFqzzeWB9wet7IqISWARcJmlRxvtMADZExBLg18D3CpaVRMQlwL8pmF8PXJW2Xw78l4L2NcClJ76rZidmVBZtNDsJXyCpgtsGfCDp18DF6fxVEdEOvC9pXcE6M4GGgtc3pqX9S9JlC0nKWhRqB1am0/8DKCzA1zG9niS8AMYA/1XSRUAbcE5B+3qSUjVmuXKQmPVOVw+Z6u7hUx+T1NBC0jzgO8DFEbFP0n/vWNaDwhpGzen3No793/02SY2zC0lGGA4XtB+b9sEsVx7aMuud54HlkorT8xZfJCmu+FuSBz8VSTqV5HGrHbYCZ6fTk4BDwEdpu2u6eJ8ikuq/AH+Ybr87k4Hd6RHRH5M8PrfDOQyN6s82wvmIxKx3niA5/7GJ5Cjh30XE+5IeA64k+YX9BsmT6T5K1/kFSbD8U0RskrSRpDrsTuCfu3ifQ8DvSFqfbmd5D/36a+AxSTeQVOc9VLDs8rQPZrly9V+zPpJUHslT8aaRHKV8Pg2ZcSS/3D+fnlvpzbYaI6K8n/r1PHBdROzrj+2ZdcVHJGZ993NJU4BS4P+IiPcBIuJjSd8jeTb2uwPZoXT47S8dIjYQfERiZmZ94pPtZmbWJw4SMzPrEweJmZn1iYPEzMz6xEFiZmZ94iAxM7M++Z9u55jhPSM2PgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0bfc0661d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 10.0)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is -0.7189960505918176\n",
      "The r2 score of LassoCV on train is 0.7550472710129603\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pxt/.local/lib/python2.7/site-packages/sklearn/linear_model/coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "\n",
    "lasso = LassoCV(alphas=alphas)    \n",
    "\n",
    "\n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "\n",
    "\n",
    "print 'The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso)\n",
    "print 'The r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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